Defect-triggered machine learning-based test generation and control

Abstract

An apparatus comprises at least one processing device configured to generate a first data structure by parsing a support ticket comprising information characterizing defects encountered while operating an information technology asset. The at least one processing device is also configured to process the first data structure utilizing a machine learning model to generate a second data structure specifying a given sequence of test steps for a given test scenario configured for testing of the defects. The at least one processing device is further configured to map the given sequence of test steps in the second data structure to respective application programming interface calls of a test automation framework, each of the application programming interface calls being associated with a functional code test unit of a test code database of the test automation framework, and to execute the given test scenario utilizing the mapped application programming interface calls.

What is claimed is: 
     
         1 . An apparatus comprising: 
 at least one processing device comprising a processor coupled to a memory;   the at least one processing device being configured: 
 to generate a first data structure at least in part by parsing a support ticket, the support ticket comprising information characterizing one or more defects encountered while operating an information technology asset;  
 to process at least portions of the first data structure utilizing a machine learning model to generate a second data structure, the second data structure specifying a given sequence of test steps for a given test scenario configured for testing of at least one of the one or more defects;  
 to map the given sequence of test steps in the second data structure to respective application programming interface calls of a test automation framework, each of the application programming interface calls being associated with a functional code test unit of a test code database of the test automation framework;  
 to verify whether the given test scenario successfully reproduces said at least one of the one or more defects based at least in part on utilizing the application programming interface calls of the test automation framework mapped to the given sequence of test steps to execute the given test scenario on a test bed having one or more test bed characteristics selected based at least in part on an operating environment of the information technology asset on which said at least one of the one or more defects was encountered; and  
 responsive to verifying that the given test scenario successfully reproduces said at least one of the one or more defects, to add the given test scenario to a test repository of the test automation framework. 
   
     
     
         2 . The apparatus of  claim 1 , wherein parsing the support ticket comprises extracting a natural language description of a root cause of at least one of the one or more defects encountered while operating the information technology asset. 
     
     
         3 . The apparatus of  claim 1 , wherein generating the first data structure comprises selecting, from at least one repository of a test management environment, one or more additional support tickets and one or more existing test scenarios generated for testing of one or more additional defects identified in the one or more additional support tickets. 
     
     
         4 . The apparatus of  claim 3 , wherein processing the first data structure utilizing the machine learning model comprises utilizing the selected one or more additional support tickets and one or more existing test scenarios for adapting the machine learning model to a testing context of the support ticket. 
     
     
         5 . The apparatus of  claim 1 , wherein the machine learning model comprises a large language model. 
     
     
         6 . (canceled) 
     
     
         7 . The apparatus of  claim 1 , wherein the operating environment of the information technology asset comprises at least one of a hardware and a software configuration of the information technology asset. 
     
     
         8 . The apparatus of  claim 1 , wherein the operating environment of the information technology asset comprises one or more workloads running on the information technology asset. 
     
     
         9 . (canceled) 
     
     
         10 . (canceled) 
     
     
         11 . The apparatus of  claim 1 , wherein adding the given test scenario to the test repository of the test automation framework comprises assigning a priority to 
       the given test scenario, the priority assigned to the given test scenario being based at least in part on the support ticket. 
     
     
         12 . The apparatus of  claim 1 , wherein the at least one processing device is further configured to determine whether the given test scenario has any testing gaps for said at least one of the one or more defects. 
     
     
         13 . The apparatus of  claim 12 , wherein the at least one processing device is further configured, responsive to determining that the given test scenario has one or more testing gaps for said at least one of the one or more defects, to update the first data structure and re-process the updated first data structure utilizing the machine learning model to generate an updated second data structure. 
     
     
         14 . The apparatus of  claim 1 , wherein the at least one processing device is further configured, responsive to determining that the given test scenario does not successfully reproduce said at least one of the one or more defects, to update the support ticket with at least one (i) additional root cause information for said at least one of the one or more defects and (ii) results of execution of the given test scenario. 
     
     
         15 . A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device: 
       to generate a first data structure at least in part by parsing a support ticket, the support ticket comprising information characterizing one or more defects encountered while operating an information technology asset; 
       to process at least portions of the first data structure utilizing a machine learning model to generate a second data structure, the second data structure specifying a given sequence of test steps for a given test scenario configured for testing of at least one of the one or more defects; 
       to map the given sequence of test steps in the second data structure to respective application programming interface calls of a test automation framework, each of the application programming interface calls being associated with a functional code test unit of a test code database of the test automation framework;  
       to verify whether the given test scenario successfully reproduces said at least one of the one or more defects based at least in part on utilizing the application programming interface calls of the test automation framework mapped to the given sequence of test steps to execute the given test scenario on a test bed having one or more test bed characteristics selected based at least in part on an operating environment of the information technology asset on which said at least one of the one or more defects was encountered and responsive to verifying that the given test scenario successfully reproduces said at least one of the one or more defects, to add the given test scenario to a test repository of the test automation framework. 
     
     
         16 . The computer program product of  claim 15 , wherein the machine learning model comprises a large language model. 
     
     
         17 . (canceled) 
     
     
         18 . A method comprising: 
       generating a first data structure at least in part by parsing a support ticket, the support ticket comprising information characterizing one or more defects encountered while operating an information technology asset; 
       processing at least portions of the first data structure utilizing a machine learning model to generate a second data structure, the second data structure specifying a given sequence of test steps for a given test scenario configured for testing of at least one of the one or more defects; 
       mapping the given sequence of test steps in the second data structure to respective application programming interface calls of a test automation framework, each of the application programming interface calls being associated with a functional code test unit of a test code database of the test automation framework;  
       verifying whether the given test scenario successfully reproduces said at least one of the one or more defects based at least in part on utilizing the application programming interface calls of the test automation framework mapped to the given sequence of test steps to execute the given test scenario on a test bed having one or more test bed characteristics selected based at least in part on an operating environment of the information technology asset on which said at least one of the one or more defects was encountered and responsive to verifying that the given test scenario successfully reproduces said at least one of the one or more defects, adding the given test scenario to a test repository of the test automation framework; wherein the method is performed by at least one processing device comprising a processor coupled to a memory. 
     
     
         19 . The method of  claim 18 , wherein the machine learning model comprises a large language model. 
     
     
         20 . (canceled) 
     
     
         21 . The computer program product of  claim 15 , wherein the program code when executed by the at least one processing device further causes the at least one processing device to determine whether the given test scenario has any testing gaps for said at least one of the one or more defects. 
     
     
         22 . The computer program product of  claim 16 , wherein the program code when executed by the at least one processing device further causes the at least one processing device, responsive to determining that the given test scenario has one or more testing gaps for said at least 
       one of the one or more defects, to update the first data structure and to re-process the updated first data structure utilizing the machine learning model to generate an updated second data structure. 
     
     
         23 . The method of  claim 18 , further comprising determining whether the given test scenario has any testing gaps for said at least one of the one or more defects. 
     
     
         24 . The method of  claim 23 , further comprising, responsive to determining that the given test scenario has one or more testing gaps for said at least one of the one or more defects, updating the first data structure and re-processing the updated first data structure utilizing the machine learning model to generate an updated second data structure. 
     
     
         25 . The method of  claim 18 , further comprising, responsive to determining that the given test scenario does not successfully reproduce said at least one of the one or more defects, updating the support ticket with at least one (i) additional root cause information for said at least one of the one or more defects and (ii) results of execution of the given test scenario.